Location determination using RF fingerprinting

ABSTRACT

A method for determining the location of a mobile unit (MU) in a wireless communication system and presenting it to a remote party. The location of a remote MU is determined by comparing a snapshot of a predefined portion of the radio-frequency (RF) spectrum taken by the MU to a reference database containing multiple snapshots taken at various locations. The result of the comparison is used to determine if the MU is at a specific location. The comparison may be made in the MU, or at some other location situated remotely from the MU. In the latter case, sufficient information regarding the captured fingerprint is transmitted from the MU to the remote location. The database may be pre-compiled or generated on-line.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/128,128, filed 22 Apr. 2002 (to issue on Aug. 24, 2004 as U.S. Pat.No. 6,782,265), which is continuation of U.S. patent application Ser.No. 09/532,418, filed 22 Mar. 2000 (now U.S. Pat. No. 6,393,294), whichis a continuation-in-part of U.S. patent application Ser. No.09/158,296, filed 22 Sep. 1998 (now U.S. Pat. No. 6,269,246). All ofthese applications are incorporated by reference.

BACKGROUND OF THE INVENTION

The present invention relates generally to telecommunications, and morespecifically to wireless communication systems.

In connection with mobile communication systems, it is becomingincreasingly important to determine the location of the communicatingMobile Unit (MU). Various systems for locating are already well known.One solution that is readily available in most modern cellular systemsis to use the ID of the cell from which the MU is communicating.Typically, this information is accurate to a resolution of severalmiles. A second solution is to compute the location of the MU based onthe cellular network signaling parameters (angle of arrival or timedifference of arrival). This information is typically accurate tohundreds of meters. Yet another solution is to equip the MU with a GPSreceiver which then attempts to track the location of the MU asaccurately as possible. Typically, GPS receivers can compute locationsto within several tens of meters of accuracy. When combined withdifferential corrections, the GPS accuracy can be improved.

As far as reliability is concerned, the cell ID information is the mostreliable, and is guaranteed to be available as long as the cellularnetwork is functioning normally. The network signal based locationcomputations are less reliable, since they are dependent on severalconditions being true at the time of the call. For example, most schemesrequire the MU to have line-of-sight visibility to multiple cellularbase stations. This is not always possible. GPS based locationcomputation is also not always reliable since the MU may be in anenvironment where there is no penetration of the GPS satellite signals.

SUMMARY OF THE INVENTION

The present invention provides a method for determining the location ofa mobile unit (MU) in a wireless communication system and presenting itto a remote party.

According to one aspect of the invention location of a remote MU isdetermined by comparing a snapshot of a predefined portion of theradio-frequency (RF) spectrum taken by the MU to a reference databasecontaining multiple snapshots taken at various locations. The result ofthe comparison is used to determine if the MU is at a specific location.The comparison may be made in the MU, or at some other location situatedremotely from the MU. In the latter case, sufficient informationregarding the captured fingerprint is transmitted from the MU to theremote location. The database may be pre-compiled or generated on-line.

The invention also provides methods for generating an RF fingerprintdatabase.

A further understanding of the nature and advantages of the presentinvention may be realized by reference to the remaining portions of thespecification and the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a representative wireless communication system;

FIG. 2 is a high level diagram of the Mobile Unit;

FIG. 3 is a flow diagram of the position determining process employed bythis invention;

FIG. 4 is a block diagram showing the technique for implementingcorrections for co-channel and adjacent-channel interference;

FIG. 5 is a block diagram describing the technique for calibrating thepredicted fingerprint database using field measurements;

FIG. 6 is a block diagram showing the operation of the recursiveprocessing Markov algorithm;

FIG. 7 describes the Markov transition probabilities;

FIG. 8 is a block diagram showing the operation of the multiplehypothesis testing algorithm;

FIG. 9 is an illustration of the organization of the fingerprint data;and

FIG. 10 is an illustration of the organization of the fingerprintdatabase.

DETAILED DESCRIPTION

The present invention provides a new method for determining the locationestimate of a Mobile Unit (MU) in a wireless communication network.

FIG. 1 is a high level block diagram of a wireless communicationnetwork. A Mobile Unit 10 has a connection with a wireless network 15,which in turn is connected to an Other Party 30. The Other Party may ormay not be mobile. The location of the MU is of interest to the OtherParty for several reasons such as provisioning of prompt and efficientpersonalized services, dispatching emergency assistance personnel,tracking the movements of the MU, etc.

There are several different prior art methods for determining thelocation of MU 10, as is known to one skilled in the art. For example,the MU could be equipped with a GPS receiver. Alternatively, thewireless network could be equipped to determine the location of MU 10.For example, the network could monitor the time of arrival of signalsfrom the MU at various nodes and from that information determine itslocation. Again, such techniques are well known to one skilled in theart.

All of the prior art techniques have significant disadvantages. Forexample, it is well known that GPS receivers do not work very well inurban canyons and indoor locations where signal strength is very low.The network based schemes such as TDOA and AOA (both well known priorart) are disadvantaged in that they need significant infrastructuremodifications.

The present invention provides a new method for determining the locationof MU 10 which (a) works in areas where GPS coverage is not typicallyavailable, and (b) does not require any infrastructure modifications.Thus, the present invention complements existing location determiningtechnologies and, when used in conjunction with them, augments theirperformance.

The invention is based on the principle that any location has a uniqueRadio Frequency (RF) spectral fingerprint. Spectral fingerprint in thiscontext is defined as a predetermined combination of observable RFspectral parameters. For instance, observed signal strength of apredetermined set of signals in the RF spectrum constitutes afingerprint. Today, worldwide, practically the entire RF spectrum, up to2 GHz and above, is being utilized by several different applications.The signal characteristics vary greatly across this spectrum. However,for any given location, it is possible to pre-select a portion of thespectrum and a combination of signal parameters in the pre-selected bandthat will be unique to that location.

In accordance with the invention MU 10 is equipped with circuitry andsoftware that is capable of capturing information from predeterminedportions of the RF spectrum. In one embodiment the predeterminedportions of the RF spectrum all fall within or in close proximity to thesame band as that utilized by the wireless communication network. Insuch an instance the same hardware circuitry can be used for performingboth functions. In another embodiment the predetermined portions of theRF spectrum are different from the wireless communication band and insuch an instance additional circuitry is required. For example, the MUmay use signal characteristics from the television UHF band, in whichcase it will require a television tuner capable of capturing theappropriate television channels. In another example the MU is equippedwith a tuner designed to capture AM or FM radio broadcasts. In this casethe MU is equipped with a radio capable of tuning to the appropriateradio broadcasting bands.

FIG. 2 shows the MU containing a component 101 for tuning to apredetermined portion of the RF spectrum. Also included is acommunication component 105 for communicating information with the OtherParty over an existing wireless infrastructure. Component 101 obtainsinformation from the RF spectrum via an Antenna 102. In one embodimentof the system, the communication link between the MU and Other Party isthrough the base stations and base station controllers of the cellularnetwork. In another embodiment of the system, data is communicated inboth directions between the MU and Other Party by using the ShortMessaging System (SMS) of the network. Using SMS messages forimplementation of this invention has the advantage of avoiding potentialinterference with voice channel network operations.

In many instances, Other Party 30 is interested in only determining ifMU 10 is at a particular location or not. The resolution of knowing theMU's location is not high (e.g., several meters), but much coarser, suchas of the order of several tens of meters. For example, Other Party 30may be interested in knowing if MU 10 is inside a particular building,or a campus or a block. In such cases it is not necessary to providevery high-resolution information to Other Party 30.

There are other instances where Other Party 30 is desirous of knowingthe accurate location of MU 10, however, is incapable of doing so. Thiscould be because other location determining capabilities in the system,such as GPS, are not functional at the instant when the locationinformation is desired. This is typical when the MU is in an area whereGPS signals are not available, such as inside a building. The locationdetermining method described in this invention is capable of operatingin areas where GPS and other location technologies are not.

When a location estimate of the MU is desired (either by itself or bythe Other Party), it activates component 101 (FIG. 2), which capturespredetermined information from a predetermined portion of the RFspectrum. Instructions regarding what information to capture and theportion of the RF spectrum from which to capture may be eitherpre-programmed in the MU, or generated in real time. In the latter case,it may be generated in the MU, or downloaded into the MU from the OtherParty over the wireless network. The MU may capture multiple pieces ofinformation or from multiple portions of the spectrum.

The spectral fingerprint may be generated using many differentparameters, either individually or in combination. In one embodiment,signal strength is used. In another embodiment, phase information isused. In another embodiment, the identity of the received signals (e.g.,frequency) is used. In yet another embodiment, the identity of thesignal source (e.g., channel number or station code) is used. In yetanother embodiment, the geographic locations of the transmitters fromwhich the signals originate are used.

A mobile cellular channel is in general frequency selective, i.e., itsproperties vary over the bandwidth used. The variation depends on theenvironment, because of multipath signals arriving at different delays.For a GSM signal with a bandwidth about 250 kHz, the dispersion can befelt in urban and hilly environments. It causesinter-symbol-interference, which results in digital errors unlesscorrected. The time dispersion is often expressed as the delay spread orthe standard deviation of the impulse response. The remedy in a receiveris to have an equalizer, typically a transversal filter with some tapsthat can be adjusted. This filter will remove theinter-symbol-interference. The filter's tap values depend on theenvironment and impulse response, and thus in principle add additionalinformation to the RF fingerprint. For future systems which use W-CDMA(wideband code division multiple access), the bandwidth is more than 10times higher, of the order 4-5 MHz. The resolution power is thus muchhigher, and the various filter taps contain much more information. Inthis embodiment of the invention, the spectral characteristics withinthe used bandwidth (and not just individual center frequencies), asmeasured by the equalizer filter, will be included in the RFfingerprint.

The MU is equipped with the appropriate circuitry and software tocapture the required signals and their parameters. In one embodiment theMU has an antenna that is designed to have a bandwidth spanning a largeportion of the VHF and UHF spectrum, e.g., from 70 MHz to 1 GHz. Inanother embodiment, the MU has an antenna that is designed to captureonly a narrowband of the spectrum. Such an antenna may be cheaper toimplement and unobtrusive. In one embodiment the MU is equipped withappropriate circuitry to determine the strength of the received signal.In one instance the location of the transmitter is broadcast in thesignal and is extracted in the MU.

In one embodiment, the MU is instructed by the Other Party to scanselected portions of the spectrum and capture selected parameters fromthe received signals. The Other Party determines which portions of thespectrum to scan and what parameters to capture based on otherinformation it has received or generated regarding the MU. For example,in one instance the Other Party knows the approximate location of the MUby receiving identity of the (wireless communication network) cell thatthe MU is in at that time. By looking up a database the Other Party candetermine the geographic location of the cell. The Other Party thendetermines which signals in the vicinity of said cell are most suitablefor generating a fingerprint. For example, certain television signalsmay have better coverage of the cell than other signals. The Other Partythen transmits this information (e.g., television channel numbers) tothe MU via the wireless link requesting it to scan only those selectedsignals.

In another embodiment, the MU determines which portion of the spectrumto scan, and what parameters to use for generating the fingerprint.

After the MU captures the appropriate signals and extracts theparameters, it has the basic information for generating the fingerprint.Some preprocessing may be required to refine the raw data. For example,signal strengths may have to be lower and upper limited to eliminatevery weak and very strong signals.

Once the fingerprint is generated, its association with a certainlocation has to be determined. According to this invention this is doneby utilizing a fingerprint database that contains a number offingerprints along with their corresponding location identities. In oneembodiment the database is stored in the MU. The generated fingerprintis compared with the fingerprints in the database and the fingerprint inthe database that is closest to the generated fingerprint is selected asthe match. The corresponding location in the database is then chosen asthe location of the MU. In one embodiment, the search algorithm takesmore than one fingerprint from the database that are closest to thegenerated fingerprint and interpolates the most plausible location forthe MU from the locations of the chosen fingerprints.

In another embodiment, the fingerprint database is stored at the OtherParty and the generated fingerprint (in the MU) is transmitted to theOther Party over the wireless link. The search for the closestfingerprint is then done in the Other Party from which it determines thelocation of the MU.

FIG. 3 depicts the flow of events in this case. A request for positionof the MU is generated, as shown in box 301. The request may begenerated by the user carrying the MU, or remotely by the Other Party.On receipt of the request the MU captures the fingerprint of its currentlocation (box 303). The captured fingerprint is processed appropriately.Processing may include filtering the fingerprint data and reformattingit to reduce its storage space. Subsequently the fingerprint istransmitted over the wireless link to the Other Party as shown in box305. The Other Party has a database into which it executes a search forthe closest matching fingerprint, as shown in box 307. Box 309 shows theprocess culminating in the retrieval of the best matching fingerprintalong with its corresponding location. In one embodiment the search alsoreturns a confidence measure that depicts the closeness of the match.

According to one implementation, the fingerprint database is designed tocompensate for any co-channel and adjacent-channel interference measuredby the mobile unit (MU). This channel interference can result fromeither control channels or traffic channels. To improve the accuracy, aswell as viability, of any algorithm for MU location based onmeasurements of base station signal strengths, corrections for channelinterference will be required. These interference corrections arecalculated and updated off-line using RF prediction models (the samemodels employed to generate the original fingerprint database) and thewireless carrier's frequency/channel allocation plan (FCAP). In oneembodiment of the invention, the interference corrections areimplemented in the fingerprint database, which is stored in the OtherParty (i.e., Location Server).

FIG. 4 describes the data flow and processing requirements for thistechnique. The data required from the wireless carrier's FCAP is asfollows: cell global identifiers, frequency identifiers for the assignedbase station control channels, and frequency identifiers for theassigned traffic channels. Co-channel interference sources areidentified using the FCAP to identify base stations that are assignedthe same frequency channel; the corrected signal strengths in eachchannel are then calculated. Adjacent-channel interference sources areidentified using the FCAP to identify base station pairs that areassigned adjacent frequency channels; the corrected signal strengths arethen calculated by also using the MU's filter discrimination ratio. Theresulting interference-corrected fingerprints are stored in thefingerprint database.

According to one implementation, the fingerprint database is generatedby computer calculations with RF prediction models using certainparameters which depend on the type of buildings, terrain features, timeof the day, environmental and seasonal conditions, etc. The calculationsare based upon theoretical models which have general applicability, butcannot consider all of the details in the real world. The accuracy ofthe fingerprint database can be enhanced by comparing the modelpredictions with field measurements as shown in FIG. 5. The adaptationalgorithm analyzes the nature of the difference between the predictedand measured fingerprints and determines the best way of changing themodel parameters to reduce the difference. Since the database consistsof multiple frequencies, the difference is a multi-dimensional vector.The minimization of the difference can use a combination of advancedcomputational techniques based upon optimization, statistics,computational intelligence, learning, etc.

The adaptation algorithm also determines what measurements to collect tobest calibrate the RF prediction model. Instead of collecting allmeasurements, only measurements that can provide the maximal informationto support reduction of the prediction errors are collected. Thespecific choice of measurements to collect is based upon an analysis ofthe nature of the error and the desired accuracy of the predictionmodel. The algorithm determines not just the locations for measurementcollection, but also the time of collection, and the number ofmeasurement samples needed to achieve a certain degree of accuracy basedupon statistical considerations.

To improve the accuracy of the fingerprint database, learning/trainingtechniques and methodologies can be adopted for this problem. For thisembodiment of the invention, either Support Vector Machines forRegression or Regularized Regression can be used to learn each mappingof position versus fingerprint (measured or predicted signal strengths).Position will be used as the input value, while signal strength will beused as the target value. The complete grid of locations for thecellular service area will be used as training data. The final result isa learned relationship between position and signal strength for eachmapping so that, given an arbitrary position, the corresponding signalstrength is obtained for each particular frequency channel.

The inputs of the training step are a set of positions matched with thecorresponding signal strengths, and the fingerprint measurements whoseinformation should be considered in the learning process are added tothe training set. The machine is then re-trained and a new grid isgenerated for the database, to replace the old one. The following listsummarizes the major advantages of learning/training techniques: (1)they offer a way to interpolate between grid points and reconstruct thegrid, together with metrics to relate measurements and expectedfingerprints; (2) a local improvement of the neighboring area resultsfrom adding one measurement to the training set, and not only theparticular location is affected, therefore, there is no need to obtainmeasurements for the complete grid; (3) measurements taken at differenttimes can help to produce specific databases for different times of theday, different weather conditions, or different seasons; and (4) thesystem can learn which are the ambiguous areas with an intelligentagent, and collect measurements for them, so the database can becontinually improved.

According to one implementation, the fingerprint database is designed totake into account any dynamic, but predetermined, variations in the RFsignal characteristics. For example, it is not uncommon that some AMradio broadcast stations lower their transmitter power at night tominimize interference with other stations. In some countries this ismandated by law. If signal strength is one of the parameters used forgenerating the fingerprint, then it is essential that the dynamic changein transmitted power be taken into consideration before any decision ismade. According to this aspect of the invention, the fingerprintdatabase and the decision algorithms are designed to accommodate suchdynamic changes. Since the change pattern in signal characteristics ispredetermined, the database is constructed by capturing the fingerprintsat different times so as to cover all the different patterns in thetransmitted signals. The time at which a fingerprint was captured isalso stored along with its location identity.

There are many choices for the search algorithm that is required todetermine the closest matching fingerprint, as can be appreciated by oneskilled in the art of statistical pattern matching. Specifically, thechoice of the algorithm is a function of what parameters are used togenerate the fingerprints. In one instance the search algorithm choosesthe fingerprint from the database that has the smallest mathematicaldistance between itself and the captured fingerprint. The mathematicaldistance is defined as a norm between the two data sets. For example, itcould be the average squared difference between the two fingerprints.There are many different ways to define “closeness” between twofingerprints; again, this is dependent on the signal parameters used togenerate the fingerprints. In one embodiment the search algorithm alsohas built in heuristics that make the best possible decision in casenone of the fingerprints in the database matches well with the generatedfingerprint.

Mobile unit (MU) location determination that separately processes singletime samples of the first fingerprints (i.e., measured fingerprints)will herein be referred to as “static location,” which has been shown tobe an inherently inaccurate solution. Using a processing approach basedon the time series nature of the measured fingerprints, herein referredto as “dynamic location,” can yield significant improvements in locationaccuracy with respect to static location techniques. In this embodimentof the invention, a dynamic location technique, which is based on Markovtheory and exploits the time history of the measured fingerprints, isdescribed in FIGS. 6 and 7. The procedure uses a predicted fingerprintmodel and a time series of fingerprint measurements from the MU tocalculate the probability distribution of the MU's location. Theprobability distribution is then used to compute an estimate of thecurrent location of the MU using one of several statistical methods.

As shown in FIG. 6, the processing algorithm is recursive and uses apredefined array of possible locations. When the algorithm has processedthe time series of fingerprint measurements currently available, it willhave calculated the probability that the MU is at each point in thearray of possible locations (the updated probability distribution). Thisprobability distribution is the basic state of the algorithm thatincorporates all of the information that has been collected on the MU upto the current time. When a new fingerprint measurement becomesavailable, the algorithm uses its knowledge of the possible motions ofthe MU during the time period between the last measurement and the newmeasurement to predict a new location probability distribution (thepredicted distribution). This prediction algorithm is based on a Markovprocess model, which uses the probability that the unit is at eachlocation and the probability that it will transition from one locationto another to calculate the probability that it will be at each locationat the next measurement time (see FIG. 7). Next, the algorithm uses thepredicted signature model and the new signature measurement to calculatea location probability distribution based on the new measurement alone.This probability distribution is then combined with the predicteddistribution to produce a new updated distribution that now incorporatesthe information provided by the new measurement.

Before any measurements are available, the algorithm starts with aninitial probability distribution that assumes that the MU is equallylikely to be at any location in the cell for the base station handlingthe call. The location probability distribution (either updated orpredicted) may be used to compute an estimate of the MU's locationwhenever such an estimate is needed. The basic algorithm calculates boththe expected location and the most probable location, but other locationstatistics (as deemed appropriate) could also be calculated from thelocation probability distribution. The location estimate may becalculated for either the time of the last fingerprint measurement(which yields a filtered estimate) or for some time before the lastfingerprint measurement (which yields a smoothed estimate). The smoothedestimate is more accurate than the filtered estimate, because it usesmore information (i.e., some number of subsequent measurements).However, the smoothed estimate requires both a larger state and morecomputation than the filtered estimate.

Matching the measured fingerprints to the fingerprint database may yieldmultiple location possibilities in the presence of measurement andmodeling errors. A single hypothesis approach that selects the bestlocation estimate for the MU using only the current measured fingerprintis likely to produce erroneous results. In this embodiment, a multiplehypothesis testing technique uses measured fingerprints from multipletimes, the fingerprint database, and MU motion constraints to generatemultiple hypotheses for MU locations over time and then selects the besthypothesis based upon statistical measures.

In FIG. 8, the measured fingerprints are compared with the fingerprintdatabase to generate possible MU location hypotheses. When previouslocation hypotheses are available, these hypotheses are updated with thelatest measurements to form new hypotheses. The number of hypotheses isreduced by considering only those locations that are consistent with theprevious location hypotheses. A previous location hypothesis may yieldmultiple new hypotheses because of possible new locations. However, someprevious location hypotheses may not have consistent new locations basedon the new measurements. A hypothesis evaluation algorithm then assignsa score to each of the updated location hypotheses. This score measureshow well the locations at different times in the hypothesis areconsistent with reasonable MU motion and measurements characteristics.The evaluation of this score is based upon statistical techniques anduses MU motion constraints, the fingerprint database, and the measuredfingerprint characteristics. The score also depends on the score of theprevious hypothesis. The location hypotheses are then ranked accordingto this score. A hypothesis management algorithm reduces the number ofhypotheses by removing hypotheses with low scores and combining similarhypotheses. The surviving hypotheses are then updated with newmeasurements.

The complexity of the search can be greatly reduced if an approximateestimate of the MU's location is already available. For example, if theidentity of the cell in which the MU is located is known, then accordingto this invention, the search algorithm will limit its search to onlythose fingerprints that correspond to locations contained within saidcell.

Similarly, the search complexity is reduced by noting the time at whichthe location information is requested. As previously mentioned, not allfingerprints in the database are valid for all times in the day. Knowingthe time at which the request is received, the database engine limitsthe search to the appropriate fingerprints.

FIG. 9 illustrates a structure 400 of the fingerprint in one embodiment.As mentioned previously there are several possible methods for definingthe fingerprint; FIG. 9 is but an example. The time at which thefingerprint is captured is stored in the fingerprint structure, as shownby box 401. In one embodiment the UTC format is used to store time.There are several fields in the structure, some of which are optionallyfilled by the MU. Some other fields are optionally filled by the OtherParty. It is not necessary that all fields be filled, since thenecessary fields can be predetermined based on system parameters.

The fingerprint comprises characteristics of received signals atmultiple frequencies. Each column in FIG. 9 is information pertaining toa particular frequency or carrier. A Station ID field 403 indicates theunique identifying code of a broadcasting station from which the signalemanated. This field is optional. In one embodiment this field is filledby the MU using information received in the signal. In anotherembodiment this field is filled by the Other Party to indicate to the MUas to which signals to capture for the fingerprint. A Frequency field405 is the unique frequency value at which a signal is captured. Eitherthe Station ID field or the Frequency field is mandatory since withoutboth it is not possible to identify the signal. A Tuning Parameter field407 is used when the MU requires additional information to tune to aparticular carrier. In one embodiment this field is supplied by theOther Party with information containing the modulation characteristicsof the signal. This field is optional. In one embodiment a TransmitterLocation field 409 is used to characterize the received signals. Inanother embodiment this field is filled by the Other Party. The MU mayoptionally use this information to determine if it wants to capture thesignal emanating from a particular transmitter. Finally, Signal Strengthfields 411, 413, are filled by the MU based on the signal strengths ofthe received carriers. In one embodiment the signal strength is sampledmultiple times for each frequency in order to smooth out any variations.At least one of the Signal Strength fields is required to be filled bythe MU.

FIG. 10 shows the high level structure of the fingerprint database 501in one embodiment. As one skilled in the art can appreciate, there aremany methods for building, managing and searching databases. The purposeof FIG. 10 is merely to illustrate the structure of the database in oneembodiment. Each row in database 501 corresponds to one fingerprint. TheLat and Long fields indicate the latitude and longitude of the locationto which the fingerprint corresponds. In one instance the fingerprintcorresponds not to one exact spot on the surface of the earth, butinstead to a small area. The Lat and Long fields in this embodimentindicate a position inside the area, preferably the center point. TheTime column indicates the time at which the fingerprint was captured. Inone embodiment the UTC time format is used to indicate this time. TheFingerprint column contains the actual fingerprint data. In oneembodiment the structure depicted in FIG. 9 is used to store thefingerprint data. Finally, the Description column contains a shortdescription of the location corresponding to the fingerprint. Forexample, it may indicate a street address, or an intersection. Thisfield is optional.

In one embodiment, the database is built by taking off-line snapshots offingerprints at various locations. The fingerprint information alongwith the coordinates of the location are entered into the database. Themore the locations, the richer the database. The resolution of locationdetermination is also controlled by how far apart the fingerprintsamples are taken. The closer they are, the higher the resolution. Ofcourse, a person skilled in the art can appreciate that the resolutionof the database is limited by the sensitivity of the fingerprintmeasuring device. In one embodiment the fingerprints are taken usingvery sensitive signal measuring devices that enable locations that arevery close to each other to have distinct fingerprints.

In another embodiment, the database is built by taking fingerprintmeasurements at predetermined locations and using intelligent algorithmsthat interpolate the fingerprints at all locations in between thesampled locations. This method has the advantage of not requiring agreat many physical measurements to be made, however, it does sufferfrom some loss in accuracy. This is because, however clever, theinterpolating algorithms will not be as accurate as making the actualmeasurements.

In yet another embodiment, the database is generated on-line using smartalgorithms that can predict the fingerprints in a local area. Thisscheme is effective in instances where an approximate idea of the MUlocation is already available. For example, this could be the cell inwhich the MU is located.

Location estimation procedures using a predicted fingerprint model andfingerprint measurements from the MU are subject to several sources oferror, including errors in measuring the fingerprints and mismatchesbetween the fingerprint model and the real world. If the predictedfingerprints in two locations are similar, either of these error sourcesmay cause the measured fingerprint to more closely match the fingerprintmodel for the incorrect location than for the correct location. Thissituation leads to the possibility of fingerprint ambiguities.

For this embodiment of the invention, when a fingerprint model isproduced for a particular region, it can be subjected to statisticalanalysis to assess both the location accuracy that may be expected inthat region and to identify specific areas that can have potentialambiguity problems. For each point in the fingerprint model, theprobability distribution of ambiguous locations may be calculated, as afunction of the level of errors expected, by computing the likelihoodthat each pair of location points will be confused at that error level.The characteristics of this distribution provide information about theaccuracy and confidence of location estimates. If the distribution at aparticular point is tight (low statistical variance), location estimatesof MU's at that location should be quite accurate. If the distributionat a particular point is very broad (high statistical variance),location estimates of MU's at that location are likely to be inaccurate.Conversely, if the calculated location estimate is at a point known tohave high ambiguity (high statistical variance), the confidenceattributed to that estimate should be low.

The following references are incorporated by reference in their entiretyfor all purposes:

-   T. Suzuki, et al., The Moving-body Position Detection Method; Patent    Application (Showa 63-195800), August 1988.-   M. Hellebrandt, et al., Estimating Position and Velocity of Mobiles    in a Cellular Radio Network, IEEE Trans. On Vehicular Technology,    Vol. 46, No. 1, pp. 65-71, February 1997.-   I. Gaspard, et al., Position Assignment in Digital Cellular Mobile    Radio Networks (e.g. GSM) Derived from Measurements at the Protocol    Interface, IEEE Journal, pp. 592-596, March 1997.-   J. Jimenez, et al., Mobile Location Using Coverage Information:    Theoretical Analysis and Results, COST 259 TD(98), April 1999.

CONCLUSION

In conclusion, it can be seen that this invention has one or more of thefollowing significant improvements over prior art for MU locationtechniques: (1) it can be implemented without requiring anymodifications to the existing wireless network infrastructure, (2) onlyminor software changes in the MU (typically a cellular phone) arerequired, and (3) it can be utilized in areas where GPS coverage iseither not available or not reliable. This follows because thefingerprints are generated by using portions of the RF spectrum thattypically have superior coverage and in-building penetration than GPSsignals.

While the above is a complete description of specific embodiments of theinvention, various modifications, alternative constructions, andequivalents may be used. Therefore, the above description should not betaken as limiting the scope of the invention as defined by the claims.

1. A method comprising: capturing, at a mobile unit: (i) a firstsnapshot of a first signal at a first time, wherein the first signal isreceived by the mobile unit, and (ii) a second snapshot of a secondsignal at a second time, wherein the second signal is received by themobile unit; generating, at a location server: (i) a first result basedon a comparison of the first snapshot with the contents of a database ofRF fingerprints that associates RF fingerprints with locations, and (ii)a second result based on a comparison of the second snapshot with thecontents of the database of RF fingerprints; estimating, at the locationserver, a location of the mobile unit based on: (i) the first result,(ii) the second result, (iii) the amount of time between the first timeand the second time, and (iv) a model of the probable motion of themobile unit between the first time and the second time.
 2. The method ofclaim 1 wherein estimating the location of the mobile unit is also basedon whether the first time occurs before the second time.
 3. The methodof claim 1 wherein the first signal and the second signal are receivedby the mobile unit through the same antenna at different times.
 4. Amethod comprising: receiving, at a location server: (i) a first snapshotof a first signal, wherein the first signal was received by a mobileunit at a first time, and (ii) a second snapshot of a second signal,wherein the second signal was received by the mobile unit at a secondtime; generating, at the location server: (i) a first result based on acomparison of the first snapshot with the contents of a database of RFfingerprints that associates RF fingerprints with locations, and (ii) asecond result based on a comparison of the second snapshot with thecontents of the database of RF fingerprints; and estimating, at thelocation server, a location of the mobile unit based on: (i) the firstresult, (ii) the second result, (iii) the amount of time between thefirst time and the second time, and (iv) a model of the probable motionof the mobile unit between the first time and the second time.
 5. Themethod of claim 4 wherein estimating the location of the mobile unit isalso based on whether the first time occurs before the second time. 6.The method of claim 4 wherein the first signal and the second signal arereceived by the mobile unit through the same antenna at different times.7. The method of claim 4 wherein the location server comprises at leastone interconnected data processing systems.